starting eda

logs/08_30_2023_10_21_18.log/08_30_2023_10_21_18.log

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+[ 2023-08-30 10:21:18,328 ] 25 root - INFO - divide by zero 

notebook/EDA.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Student Performance Indicator\n",
+    "- Understanding the problem statement\n",
+    "- Data Collection'\n",
+    "- Data Checks\n",
+    "- Exploratory Data Analysis\n",
+    "- Data Pre-processing\n",
+    "- Model Training\n",
+    "- Choose the best model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1) Problem Statement\n",
+    "- This project understands how the student's performace (test score) is affected by other variables such gender, ethinicity, parental education, test preparation course.\n",
+    "\n",
+    "### 2) Data Collection\n",
+    "- Data Source - https://www.kaggle.com/datasets/spscientist/students-performance-in-exams?datasetId=74977\n",
+    "- The data consist of 8 columns and 1000 rows.\n",
+    "\n",
+    "#### 2.1 Import Data and Required Packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Import CSV data as Pandas DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('data/stud.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Show top and bottom records"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>gender</th>\n",
+       "      <th>race_ethnicity</th>\n",
+       "      <th>parental_level_of_education</th>\n",
+       "      <th>lunch</th>\n",
+       "      <th>test_preparation_course</th>\n",
+       "      <th>math_score</th>\n",
+       "      <th>reading_score</th>\n",
+       "      <th>writing_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group B</td>\n",
+       "      <td>bachelor's degree</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>none</td>\n",
+       "      <td>72</td>\n",
+       "      <td>72</td>\n",
+       "      <td>74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group C</td>\n",
+       "      <td>some college</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>completed</td>\n",
+       "      <td>69</td>\n",
+       "      <td>90</td>\n",
+       "      <td>88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group B</td>\n",
+       "      <td>master's degree</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>none</td>\n",
+       "      <td>90</td>\n",
+       "      <td>95</td>\n",
+       "      <td>93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>male</td>\n",
+       "      <td>group A</td>\n",
+       "      <td>associate's degree</td>\n",
+       "      <td>free/reduced</td>\n",
+       "      <td>none</td>\n",
+       "      <td>47</td>\n",
+       "      <td>57</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>male</td>\n",
+       "      <td>group C</td>\n",
+       "      <td>some college</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>none</td>\n",
+       "      <td>76</td>\n",
+       "      <td>78</td>\n",
+       "      <td>75</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   gender race_ethnicity parental_level_of_education         lunch  \\\n",
+       "0  female        group B           bachelor's degree      standard   \n",
+       "1  female        group C                some college      standard   \n",
+       "2  female        group B             master's degree      standard   \n",
+       "3    male        group A          associate's degree  free/reduced   \n",
+       "4    male        group C                some college      standard   \n",
+       "\n",
+       "  test_preparation_course  math_score  reading_score  writing_score  \n",
+       "0                    none          72             72             74  \n",
+       "1               completed          69             90             88  \n",
+       "2                    none          90             95             93  \n",
+       "3                    none          47             57             44  \n",
+       "4                    none          76             78             75  "
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>gender</th>\n",
+       "      <th>race_ethnicity</th>\n",
+       "      <th>parental_level_of_education</th>\n",
+       "      <th>lunch</th>\n",
+       "      <th>test_preparation_course</th>\n",
+       "      <th>math_score</th>\n",
+       "      <th>reading_score</th>\n",
+       "      <th>writing_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>995</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group E</td>\n",
+       "      <td>master's degree</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>completed</td>\n",
+       "      <td>88</td>\n",
+       "      <td>99</td>\n",
+       "      <td>95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>male</td>\n",
+       "      <td>group C</td>\n",
+       "      <td>high school</td>\n",
+       "      <td>free/reduced</td>\n",
+       "      <td>none</td>\n",
+       "      <td>62</td>\n",
+       "      <td>55</td>\n",
+       "      <td>55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group C</td>\n",
+       "      <td>high school</td>\n",
+       "      <td>free/reduced</td>\n",
+       "      <td>completed</td>\n",
+       "      <td>59</td>\n",
+       "      <td>71</td>\n",
+       "      <td>65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group D</td>\n",
+       "      <td>some college</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>completed</td>\n",
+       "      <td>68</td>\n",
+       "      <td>78</td>\n",
+       "      <td>77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group D</td>\n",
+       "      <td>some college</td>\n",
+       "      <td>free/reduced</td>\n",
+       "      <td>none</td>\n",
+       "      <td>77</td>\n",
+       "      <td>86</td>\n",
+       "      <td>86</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     gender race_ethnicity parental_level_of_education         lunch  \\\n",
+       "995  female        group E             master's degree      standard   \n",
+       "996    male        group C                 high school  free/reduced   \n",
+       "997  female        group C                 high school  free/reduced   \n",
+       "998  female        group D                some college      standard   \n",
+       "999  female        group D                some college  free/reduced   \n",
+       "\n",
+       "    test_preparation_course  math_score  reading_score  writing_score  \n",
+       "995               completed          88             99             95  \n",
+       "996                    none          62             55             55  \n",
+       "997               completed          59             71             65  \n",
+       "998               completed          68             78             77  \n",
+       "999                    none          77             86             86  "
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.tail(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.2 Data Information\n",
+    "\n",
+    "\n",
+    "- gender: sex of students\n",
+    "- race_ethinicity: ethinicity of students\n",
+    "- parental_level_of_education: parents' highest education\n",
+    "- lunch: having lunch before test\n",
+    "- test preparation course: completed of not before test\n",
+    "- math score,  reading score, writing score"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "shape of the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1000, 8)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3) Data Checks\n",
+    "- check missing values\n",
+    "- check duplicates\n",
+    "- Check Datatypes\n",
+    "- Check the number of unique values of each column\n",
+    "- Check Statistics of dataset\n",
+    "- check various categories present in the different categorical columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.1 Missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "gender                         0.0\n",
+       "race_ethnicity                 0.0\n",
+       "parental_level_of_education    0.0\n",
+       "lunch                          0.0\n",
+       "test_preparation_course        0.0\n",
+       "math_score                     0.0\n",
+       "reading_score                  0.0\n",
+       "writing_score                  0.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### observation \n",
+    "- no missing values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.2 Check Duplicates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.duplicated().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### observation\n",
+    "- no duplicates"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.3 Check Data Types"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1000 entries, 0 to 999\n",
+      "Data columns (total 8 columns):\n",
+      " #   Column                       Non-Null Count  Dtype \n",
+      "---  ------                       --------------  ----- \n",
+      " 0   gender                       1000 non-null   object\n",
+      " 1   race_ethnicity               1000 non-null   object\n",
+      " 2   parental_level_of_education  1000 non-null   object\n",
+      " 3   lunch                        1000 non-null   object\n",
+      " 4   test_preparation_course      1000 non-null   object\n",
+      " 5   math_score                   1000 non-null   int64 \n",
+      " 6   reading_score                1000 non-null   int64 \n",
+      " 7   writing_score                1000 non-null   int64 \n",
+      "dtypes: int64(3), object(5)\n",
+      "memory usage: 62.6+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### observation\n",
+    "- 5 categorical columns\n",
+    "- 3 numerical columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.4 Number of Unique Values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "gender                          2\n",
+       "race_ethnicity                  5\n",
+       "parental_level_of_education     6\n",
+       "lunch                           2\n",
+       "test_preparation_course         2\n",
+       "math_score                     81\n",
+       "reading_score                  72\n",
+       "writing_score                  77\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### observation\n",
+    "- gender, lunch and test_preparation_course are binary columns i.e. 2 unique values.\n",
+    "- race_ethinicity has 5 unique and parental_level_education has 6 unique values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.5 Check Statistics of dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>math_score</th>\n",
+       "      <td>1000.0</td>\n",
+       "      <td>66.089</td>\n",
+       "      <td>15.163080</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>57.00</td>\n",
+       "      <td>66.0</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>reading_score</th>\n",
+       "      <td>1000.0</td>\n",
+       "      <td>69.169</td>\n",
+       "      <td>14.600192</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>59.00</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>79.0</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>writing_score</th>\n",
+       "      <td>1000.0</td>\n",
+       "      <td>68.054</td>\n",
+       "      <td>15.195657</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>57.75</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>79.0</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                count    mean        std   min    25%   50%   75%    max\n",
+       "math_score     1000.0  66.089  15.163080   0.0  57.00  66.0  77.0  100.0\n",
+       "reading_score  1000.0  69.169  14.600192  17.0  59.00  70.0  79.0  100.0\n",
+       "writing_score  1000.0  68.054  15.195657  10.0  57.75  69.0  79.0  100.0"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe().T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### observation\n",
+    "- math_score has a minimum score of zero.\n",
+    "- distribution of three columns are similar in nature."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "categorical_cols: ['gender', 'race_ethnicity', 'parental_level_of_education', 'lunch', 'test_preparation_course']\n",
+      "length of categorical columns: 5\n",
+      "\n",
+      "numerical_cols: ['math_score', 'reading_score', 'writing_score']\n",
+      "length of numerical columns: 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "## define categorical and numerical columns\n",
+    "\n",
+    "categorical_cols = [col for col in df.columns if df[col].dtype=='O']\n",
+    "numerical_cols = [col for col in df.columns if col not in categorical_cols]\n",
+    "\n",
+    "print(f\"categorical_cols: {categorical_cols}\")\n",
+    "print(f\"length of categorical columns: {len(categorical_cols)}\\n\")\n",
+    "\n",
+    "print(f\"numerical_cols: {numerical_cols}\")\n",
+    "print(f\"length of numerical columns: {len(numerical_cols)}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.6 Categories of Categorical Columns\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Categories of gender ['female' 'male']: \n",
+      "\n",
+      "Categories of race_ethnicity ['group B' 'group C' 'group A' 'group D' 'group E']: \n",
+      "\n",
+      "Categories of parental_level_of_education [\"bachelor's degree\" 'some college' \"master's degree\" \"associate's degree\"\n",
+      " 'high school' 'some high school']: \n",
+      "\n",
+      "Categories of lunch ['standard' 'free/reduced']: \n",
+      "\n",
+      "Categories of test_preparation_course ['none' 'completed']: \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "for col in categorical_cols:\n",
+    "    print(\"Categories of {} {}: \\n\".format(col,df[col].unique()))    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4) Exploratory Data Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>gender</th>\n",
+       "      <th>race_ethnicity</th>\n",
+       "      <th>parental_level_of_education</th>\n",
+       "      <th>lunch</th>\n",
+       "      <th>test_preparation_course</th>\n",
+       "      <th>math_score</th>\n",
+       "      <th>reading_score</th>\n",
+       "      <th>writing_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group B</td>\n",
+       "      <td>bachelor's degree</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>none</td>\n",
+       "      <td>72</td>\n",
+       "      <td>72</td>\n",
+       "      <td>74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group C</td>\n",
+       "      <td>some college</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>completed</td>\n",
+       "      <td>69</td>\n",
+       "      <td>90</td>\n",
+       "      <td>88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group B</td>\n",
+       "      <td>master's degree</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>none</td>\n",
+       "      <td>90</td>\n",
+       "      <td>95</td>\n",
+       "      <td>93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>male</td>\n",
+       "      <td>group A</td>\n",
+       "      <td>associate's degree</td>\n",
+       "      <td>free/reduced</td>\n",
+       "      <td>none</td>\n",
+       "      <td>47</td>\n",
+       "      <td>57</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>male</td>\n",
+       "      <td>group C</td>\n",
+       "      <td>some college</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>none</td>\n",
+       "      <td>76</td>\n",
+       "      <td>78</td>\n",
+       "      <td>75</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   gender race_ethnicity parental_level_of_education         lunch  \\\n",
+       "0  female        group B           bachelor's degree      standard   \n",
+       "1  female        group C                some college      standard   \n",
+       "2  female        group B             master's degree      standard   \n",
+       "3    male        group A          associate's degree  free/reduced   \n",
+       "4    male        group C                some college      standard   \n",
+       "\n",
+       "  test_preparation_course  math_score  reading_score  writing_score  \n",
+       "0                    none          72             72             74  \n",
+       "1               completed          69             90             88  \n",
+       "2                    none          90             95             93  \n",
+       "3                    none          47             57             44  \n",
+       "4                    none          76             78             75  "
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of students with full marks in Maths: 7\n",
+      "Number of students with full marks in Writing: 14\n",
+      "Number of students with full marks in Reading: 17\n"
+     ]
+    }
+   ],
+   "source": [
+    "reading_full = df[df['reading_score'] == 100].shape[0]\n",
+    "writing_full = df[df['writing_score'] == 100].shape[0]\n",
+    "math_full = df[df['math_score'] == 100].shape[0]\n",
+    "\n",
+    "\n",
+    "print(f\"Number of students with full marks in Maths: {math_full}\")\n",
+    "print(f\"Number of students with full marks in Writing: {writing_full}\")\n",
+    "print(f\"Number of students with full marks in Reading: {reading_full}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of students with marks less than 30 in Maths: 16\n",
+      "Number of students with marks less than 30 in Writing: 10\n",
+      "Number of students with marks less than 30 in Reading: 8\n"
+     ]
+    }
+   ],
+   "source": [
+    "reading_less_30 = df[df['reading_score'] <= 30].shape[0]\n",
+    "writing_less_30 = df[df['writing_score'] <= 30].shape[0]\n",
+    "math_less_30 = df[df['math_score'] <= 30].shape[0]\n",
+    "\n",
+    "\n",
+    "print(f\"Number of students with marks less than 30 in Maths: {math_less_30}\")\n",
+    "print(f\"Number of students with marks less than 30 in Writing: {writing_less_30}\")\n",
+    "print(f\"Number of students with marks less than 30 in Reading: {reading_less_30}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### observation\n",
+    "- student have performed well in reading.\n",
+    "- students have performed worst in maths.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

notebook/data/stud.csv

@@ -0,0 +1,1001 @@
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+"male","group D","some high school","standard","completed","77","68","69"
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+"male","group A","some high school","free/reduced","completed","61","62","61"
+"female","group B","high school","standard","none","58","68","61"
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+"female","group D","some high school","free/reduced","none","48","54","53"
+"female","group E","associate's degree","standard","none","100","100","100"
+"female","group C","some high school","free/reduced","completed","65","76","75"
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+"female","group E","associate's degree","standard","none","68","76","67"
+"female","group B","bachelor's degree","standard","none","75","84","80"
+"female","group D","bachelor's degree","standard","none","89","100","100"
+"male","group C","some high school","standard","completed","78","72","69"
+"female","group A","high school","free/reduced","completed","53","50","60"
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+"female","group D","associate's degree","free/reduced","none","55","76","76"
+"female","group D","bachelor's degree","free/reduced","none","62","72","74"
+"male","group A","high school","standard","none","63","63","62"
+"female","group E","master's degree","standard","completed","88","99","95"
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+"female","group D","some college","free/reduced","none","77","86","86"

src/exception.py

@@ -1,5 +1,5 @@
 import sys
-import logging
+from src.logger import logging
 
 def error_message_detail(error, error_detail:sys):
     _,_,exc_tb = error_detail.exc_info()
@@ -18,3 +18,9 @@ class CustomException(Exception):
     def __str__(self) -> str:
         return self.error_message
     
+if __name__=="__main__":
+    try:
+        a=1/0
+    except Exception as e:
+        logging.info("divide by zero")
+        raise CustomException(e,sys)